V2x communication-based vehicle lane system for autonomous vehicles

ABSTRACT

According to some embodiments, a system receives, at a first sensor of the ADV, a first and a second V2X communication data from a first infrastructure and a second infrastructure respectively. The system determines a first distance from the ADV to the first infrastructure and a second distance from the ADV to the second infrastructure based on the first and the second V2X communication data. The system determines a relative location of the ADV to the first or the second infrastructure based on the first and the second distances and a predetermined distance between the first infrastructure and the second infrastructure. The system retrieves lane information based on the relative location of the ADV to the first or the second infrastructure. The system generates a trajectory based on the lane information to control the ADV autonomously according to the trajectory.

TECHNICAL FIELD

Embodiments of the present disclosure relate generally to operatingautonomous vehicles. More particularly, embodiments of the disclosurerelate to a vehicle to everything (V2X) communication-based vehicle lanesystem for autonomous driving vehicles (ADVs).

BACKGROUND

Vehicles operating in an autonomous mode (e.g., driverless) can relieveoccupants, especially the driver, from some driving-relatedresponsibilities. When operating in an autonomous mode, the vehicle cannavigate to various locations using onboard sensors, allowing thevehicle to travel with minimal human interaction or in some caseswithout any passengers.

Lane boundaries are critical input for autonomous driving vehicles oflevels 3 (L3) and higher. For example, for L3 ADVs, safety criticalfunctions are shifted to the vehicle to be handled based on perceivedlane boundaries surrounding the ADVs. Lane boundaries can be perceivedby a perception module of the ADVs. Alternative or additional methods toretrieve lane boundary information is necessary to improve a reliablefactor of the ADVs.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments of the disclosure are illustrated by way of example and notlimitation in the figures of the accompanying drawings in which likereferences indicate similar elements.

FIG. 1 is a block diagram illustrating a networked system according toone embodiment.

FIG. 2 is a block diagram illustrating an example of a sensor andcontrol system using by an autonomous vehicle according to oneembodiment.

FIGS. 3A-3B are block diagrams illustrating examples of a perception andplanning system used by an autonomous vehicle according to someembodiments.

FIG. 4 is a block diagram illustrating an example of a lane boundarymodule according to one embodiment.

FIG. 5 is a block diagram illustrating an example of an ADV using oneV2X module according to one embodiment.

FIGS. 6A-6C are examples of databases according to some embodiments.

FIG. 7 is a block diagram illustrating an example of an ADV using twoV2X modules according to one embodiment.

FIG. 8 is a block diagram illustrating an example of an ADV using twoV2X modules according to one embodiment.

FIG. 9 is a flow diagram illustrating a method according to oneembodiment.

FIG. 10 is a flow diagram illustrating a method according to oneembodiment.

FIG. 11 is a block diagram illustrating a data processing systemaccording to one embodiment.

DETAILED DESCRIPTION

Various embodiments and aspects of the disclosures will be describedwith reference to details discussed below, and the accompanying drawingswill illustrate the various embodiments. The following description anddrawings are illustrative of the disclosure and are not to be construedas limiting the disclosure. Numerous specific details are described toprovide a thorough understanding of various embodiments of the presentdisclosure. However, in certain instances, well-known or conventionaldetails are not described in order to provide a concise discussion ofembodiments of the present disclosures.

Reference in the specification to “one embodiment” or “an embodiment”means that a particular feature, structure, or characteristic describedin conjunction with the embodiment can be included in at least oneembodiment of the disclosure. The appearances of the phrase “in oneembodiment” in various places in the specification do not necessarilyall refer to the same embodiment.

According to one aspect, a system receives, at a first sensor of theADV, a first and a second V2X communication data from a firstinfrastructure and a second infrastructure respectively. The systemdetermines a first distance from the ADV to the first infrastructure anda second distance from the ADV to the second infrastructure based on thefirst and the second V2X communication data. The system determines arelative location of the ADV to the first or the second infrastructurebased on the first and the second distances and a predetermined distancebetween the first infrastructure and the second infrastructure. Thesystem retrieves lane information based on the relative location of theADV to the first or the second infrastructure. The system generates atrajectory based on the lane information to control the ADV autonomouslyaccording to the trajectory.

According to a second aspect, the system receiving, at a first sensorand a second sensor of the ADV, a first and a second V2X communicationdata from a first infrastructure respectively. The system determines afirst distance from the first sensor to the first infrastructure and asecond distance from the second sensor to the first infrastructure basedon the first and the second V2X communication data. The systemdetermines a relative location of the ADV to the first infrastructurebased on the first and the second distances and a predetermined distancebetween the first and the second sensors. The system retrieves laneinformation based on the relative location of the ADV to the firstinfrastructure. The system generates a trajectory based on the laneinformation to control the ADV autonomously according to the trajectory.

FIG. 1 is a block diagram illustrating an autonomous vehicle networkconfiguration according to one embodiment of the disclosure. Referringto FIG. 1, network configuration 100 includes autonomous vehicle 101that may be communicatively coupled to one or more servers 103-104 overa network 102. Although there is one autonomous vehicle shown, multipleautonomous vehicles can be coupled to each other and/or coupled toservers 103-104 over network 102. Network 102 may be any type ofnetworks such as a local area network (LAN), a wide area network (WAN)such as the Internet, a cellular network, a satellite network, or acombination thereof, wired or wireless. Server(s) 103-104 may be anykind of servers or a cluster of servers, such as Web or cloud servers,application servers, backend servers, or a combination thereof. Servers103-104 may be data analytics servers, content servers, trafficinformation servers, map and point of interest (MPOI) severs, orlocation servers, etc.

An autonomous vehicle refers to a vehicle that can be configured to inan autonomous mode in which the vehicle navigates through an environmentwith little or no input from a driver. Such an autonomous vehicle caninclude a sensor system having one or more sensors that are configuredto detect information about the environment in which the vehicleoperates. The vehicle and its associated controller(s) use the detectedinformation to navigate through the environment. Autonomous vehicle 101can operate in a manual mode, a full autonomous mode, or a partialautonomous mode.

In one embodiment, autonomous vehicle 101 includes, but is not limitedto, perception and planning system 110, vehicle control system 111,wireless communication system 112, user interface system 113, and sensorsystem 115. Autonomous vehicle 101 may further include certain commoncomponents included in ordinary vehicles, such as, an engine, wheels,steering wheel, transmission, etc., which may be controlled by vehiclecontrol system 111 and/or perception and planning system 110 using avariety of communication signals and/or commands, such as, for example,acceleration signals or commands, deceleration signals or commands,steering signals or commands, braking signals or commands, etc.

Components 110-115 may be communicatively coupled to each other via aninterconnect, a bus, a network, or a combination thereof. For example,components 110-115 may be communicatively coupled to each other via acontroller area network (CAN) bus. A CAN bus is a vehicle bus standarddesigned to allow microcontrollers and devices to communicate with eachother in applications without a host computer. It is a message-basedprotocol, designed originally for multiplex electrical wiring withinautomobiles, but is also used in many other contexts.

Referring now to FIG. 2, in one embodiment, sensor system 115 includes,but it is not limited to, one or more cameras 211, global positioningsystem (GPS) unit 212, inertial measurement unit (IMU) 213, radar unit214, a light detection and range (LIDAR) unit 215, and avehicle-to-everything (V2X) unit 216. GPS system 212 may include atransceiver operable to provide information regarding the position ofthe autonomous vehicle. IMU unit 213 may sense position and orientationchanges of the autonomous vehicle based on inertial acceleration. Radarunit 214 may represent a system that utilizes radio signals to senseobjects within the local environment of the autonomous vehicle. In someembodiments, in addition to sensing objects, radar unit 214 mayadditionally sense the speed and/or heading of the objects. LIDAR unit215 may sense objects in the environment in which the autonomous vehicleis located using lasers. LIDAR unit 215 could include one or more lasersources, a laser scanner, and one or more detectors, among other systemcomponents. Cameras 211 may include one or more devices to captureimages of the environment surrounding the autonomous vehicle. Cameras211 may be still cameras and/or video cameras. A camera may bemechanically movable, for example, by mounting the camera on a rotatingand/or tilting a platform. V2X unit 216 can include V2X transmitter(s)and sensor(s) to send/receive V2X communication data to/from surroundingvehicles, infrastructures, and/or any V2X-based devices.

Sensor system 115 may further include other sensors, such as, a sonarsensor, an infrared sensor, a steering sensor, a throttle sensor, abraking sensor, and an audio sensor (e.g., microphone). An audio sensormay be configured to capture sound from the environment surrounding theautonomous vehicle. A steering sensor may be configured to sense thesteering angle of a steering wheel, wheels of the vehicle, or acombination thereof. A throttle sensor and a braking sensor sense thethrottle position and braking position of the vehicle, respectively. Insome situations, a throttle sensor and a braking sensor may beintegrated as an integrated throttle/braking sensor.

In one embodiment, vehicle control system 111 includes, but is notlimited to, steering unit 201, throttle unit 202 (also referred to as anacceleration unit), and braking unit 203. Steering unit 201 is to adjustthe direction or heading of the vehicle. Throttle unit 202 is to controlthe speed of the motor or engine that in turn controls the speed andacceleration of the vehicle. Braking unit 203 is to decelerate thevehicle by providing friction to slow the wheels or tires of thevehicle. Note that the components as shown in FIG. 2 may be implementedin hardware, software, or a combination thereof.

Referring back to FIG. 1, wireless communication system 112 is to allowcommunication between autonomous vehicle 101 and external systems, suchas devices, sensors, other vehicles, etc. For example, wirelesscommunication system 112 can wirelessly communicate with one or moredevices directly or via a communication network, such as servers 103-104over network 102. Wireless communication system 112 can use any cellularcommunication network or a wireless local area network (WLAN), e.g.,using WiFi to communicate with another component or system. Wirelesscommunication system 112 could communicate directly with a device (e.g.,a mobile device of a passenger, a display device, a speaker withinvehicle 101), for example, using an infrared link, Bluetooth, etc. Userinterface system 113 may be part of peripheral devices implementedwithin vehicle 101 including, for example, a keyword, a touch screendisplay device, a microphone, and a speaker, etc.

Some or all of the functions of autonomous vehicle 101 may be controlledor managed by perception and planning system 110, especially whenoperating in an autonomous driving mode. Perception and planning system110 includes the necessary hardware (e.g., processor(s), memory,storage) and software (e.g., operating system, planning and routingprograms) to receive information from sensor system 115, control system111, wireless communication system 112, and/or user interface system113, process the received information, plan a route or path from astarting point to a destination point, and then drive vehicle 101 basedon the planning and control information. Alternatively, perception andplanning system 110 may be integrated with vehicle control system 111.

For example, a user as a passenger may specify a starting location and adestination of a trip, for example, via a user interface. Perception andplanning system 110 obtains the trip related data. For example,perception and planning system 110 may obtain location and routeinformation from an MPOI server, which may be a part of servers 103-104.The location server provides location services and the MPOI serverprovides map services and the POIs of certain locations. Alternatively,such location and MPOI information may be cached locally in a persistentstorage device of perception and planning system 110.

While autonomous vehicle 101 is moving along the route, perception andplanning system 110 may also obtain real-time traffic information from atraffic information system or server (TIS). Note that servers 103-104may be operated by a third party entity. Alternatively, thefunctionalities of servers 103-104 may be integrated with perception andplanning system 110. Based on the real-time traffic information, MPOIinformation, and location information, as well as real-time localenvironment data detected or sensed by sensor system 115 (e.g.,obstacles, objects, nearby vehicles), perception and planning system 110can plan an optimal route and drive vehicle 101, for example, viacontrol system 111, according to the planned route to reach thespecified destination safely and efficiently.

Server 103 may be a data analytics system to perform data analyticsservices for a variety of clients. In one embodiment, data analyticssystem 103 includes data collector 121 and machine learning engine 122.Data collector 121 collects driving statistics 123 from a variety ofvehicles, either autonomous vehicles or regular vehicles driven by humandrivers. Driving statistics 123 include information indicating thedriving commands (e.g., throttle, brake, steering commands) issued andresponses of the vehicles (e.g., speeds, accelerations, decelerations,directions) captured by sensors of the vehicles at different points intime. Driving statistics 123 may further include information describingthe driving environments at different points in time, such as, forexample, routes (including starting and destination locations), MPOIs,road conditions, weather conditions, etc.

Based on driving statistics 123, machine learning engine 122 generatesor trains a set of rules, algorithms, and/or models 124 for a variety ofpurposes. In one embodiment, for example, algorithms/model 124 mayinclude an algorithm/model to calculate relative locations of an ADVbased on V2X communication data for the ADV. The algorithm/model can beuploaded onto the ADV to be used by the ADV in real-time.

FIGS. 3A and 3B are block diagrams illustrating an example of aperception and planning system used with an autonomous vehicle accordingto one embodiment. System 300 may be implemented as a part of autonomousvehicle 101 of FIG. 1 including, but is not limited to, perception andplanning system 110, control system 111, and sensor system 115.Referring to FIGS. 3A-3B, perception and planning system 110 includes,but is not limited to, localization module 301, perception module 302,prediction module 303, decision module 304, planning module 305, controlmodule 306, routing/sampling module 307, and reference line generator309.

Some or all of modules 301-308 may be implemented in software, hardware,or a combination thereof. For example, these modules may be installed inpersistent storage device 352, loaded into memory 351, and executed byone or more processors (not shown). Note that some or all of thesemodules may be communicatively coupled to or integrated with some or allmodules of vehicle control system 111 of FIG. 2. Some of modules 301-308may be integrated together as an integrated module. For example, laneboundary module 308 and planning module 305 may be integrated as asingle module.

Localization module 301 determines a current location of autonomousvehicle 300 (e.g., leveraging GPS unit 212) and manages any data relatedto a trip or route of a user. Localization module 301 (also referred toas a map and route module) manages any data related to a trip or routeof a user. A user may log in and specify a starting location and adestination of a trip, for example, via a user interface. Localizationmodule 301 communicates with other components of autonomous vehicle 300,such as map and route information 311, to obtain the trip related data.For example, localization module 301 may obtain location and routeinformation from a location server and a map and POI (MPOI) server. Alocation server provides location services and an MPOI server providesmap services and the POIs of certain locations, which may be cached aspart of map and route information 311. While autonomous vehicle 300 ismoving along the route, localization module 301 may also obtainreal-time traffic information from a traffic information system orserver.

Based on the sensor data provided by sensor system 115 and localizationinformation obtained by localization module 301, a perception of thesurrounding environment is determined by perception module 302. Theperception information may represent what an ordinary driver wouldperceive surrounding a vehicle in which the driver is driving. Theperception can include the lane configuration (e.g., straight or curvelanes), traffic light signals, a relative position of another vehicle, apedestrian, a building, crosswalk, or other traffic related signs (e.g.,stop signs, yield signs), etc., for example, in a form of an object. Thelane configuration includes information describing a lane or lanes, suchas, for example, a shape of the lane (e.g., straight or curvature), awidth of the lane, how many lanes in a road, one-way or two-way lane,merging or splitting lanes, exiting lane, etc.

Perception module 302 may include a computer vision system orfunctionalities of a computer vision system to process and analyzeimages captured by one or more cameras in order to identify objectsand/or features in the environment of autonomous vehicle. The objectscan include traffic signals, road way boundaries, other vehicles,pedestrians, and/or obstacles, etc. The computer vision system may usean object recognition algorithm, video tracking, and other computervision techniques. In some embodiments, the computer vision system canmap an environment, track objects, and estimate the speed of objects,etc. Perception module 302 can also detect objects based on othersensors data provided by other sensors such as a radar and/or LIDAR.

For each of the objects, prediction module 303 predicts how the objectwill behave under the circumstances. The prediction is performed basedon the perception data perceiving the driving environment at the pointin time in view of a set of map/rout information 311 and traffic rules312. For example, if the object is a vehicle at an opposing directionand the current driving environment includes an intersection, predictionmodule 303 will predict whether the vehicle will likely move straightforward or make a turn. If the perception data indicates that theintersection has no traffic light, prediction module 303 may predictthat the vehicle may have to fully stop prior to enter the intersection.If the perception data indicates that the vehicle is currently at aleft-turn only lane or a right-turn only lane, prediction module 303 maypredict that the vehicle will more likely make a left turn or right turnrespectively.

For each of the objects, decision module 304 makes a decision regardinghow to handle the object. For example, for a particular object (e.g.,another vehicle in a crossing route) as well as its metadata describingthe object (e.g., a speed, direction, turning angle), decision module304 decides how to encounter the object (e.g., overtake, yield, stop,pass). Decision module 304 may make such decisions according to a set ofrules such as traffic rules or driving rules 312, which may be stored inpersistent storage device 352.

Routing module 307 is configured to provide one or more routes or pathsfrom a starting point to a destination point. For a given trip from astart location to a destination location, for example, received from auser, routing module 307 obtains route and map information 311 anddetermines all possible routes or paths from the starting location toreach the destination location. Routing module 307 may generate areference line in a form of a topographic map for each of the routes itdetermines from the starting location to reach the destination location.A reference line refers to an ideal route or path without anyinterference from others such as other vehicles, obstacles, or trafficcondition. That is, if there is no other vehicle, pedestrians, orobstacles on the road, an ADV should exactly or closely follows thereference line. The topographic maps are then provided to decisionmodule 304 and/or planning module 305. Decision module 304 and/orplanning module 305 examine all of the possible routes to select andmodify one of the most optimal route in view of other data provided byother modules such as traffic conditions from localization module 301,driving environment perceived by perception module 302, and trafficcondition predicted by prediction module 303. The actual path or routefor controlling the ADV may be close to or different from the referenceline provided by routing module 307 dependent upon the specific drivingenvironment at the point in time.

Based on a decision for each of the objects perceived, planning module305 plans a path or route for the autonomous vehicle, as well as drivingparameters (e.g., distance, speed, and/or turning angle). That is, for agiven object, decision module 304 decides what to do with the object,while planning module 305 determines how to do it. For example, for agiven object, decision module 304 may decide to pass the object, whileplanning module 305 may determine whether to pass on the left side orright side of the object. Planning and control data is generated byplanning module 305 including information describing how vehicle 300would move in a next moving cycle (e.g., next route/path segment). Forexample, the planning and control data may instruct vehicle 300 to move10 meters at a speed of 30 mile per hour (mph), then change to a rightlane at the speed of 25 mph.

Based on the planning and control data, control module 306 controls anddrives the autonomous vehicle, by sending proper commands or signals tovehicle control system 111, according to a route or path defined by theplanning and control data. The planning and control data includesufficient information to drive the vehicle from a first point to asecond point of a route or path using appropriate vehicle settings ordriving parameters (e.g., throttle, braking, and turning commands) atdifferent points in time along the path or route.

In one embodiment, the planning phase is performed in a number ofplanning cycles, also referred to as command cycles, such as, forexample, in every time interval of 100 milliseconds (ms). For each ofthe planning cycles or command cycles, one or more control commands willbe issued based on the planning and control data. That is, for every 100ms, planning module 305 plans a next route segment or path segment, forexample, including a target position and the time required for the ADVto reach the target position. Alternatively, planning module 305 mayfurther specify the specific speed, direction, and/or steering angle,etc. In one embodiment, planning module 305 plans a route segment orpath segment for the next predetermined period of time such as 5seconds. For each planning cycle, planning module 305 plans a targetposition for the current cycle (e.g., next 5 seconds) based on a targetposition planned in a previous cycle. Control module 306 then generatesone or more control commands (e.g., throttle, brake, steering controlcommands) based on the planning and control data of the current cycle.

Note that decision module 304 and planning module 305 may be integratedas an integrated module. Decision module 304/planning module 305 mayinclude a navigation system or functionalities of a navigation system todetermine a driving path for the autonomous vehicle. For example, thenavigation system may determine a series of speeds and directionalheadings to effect movement of the autonomous vehicle along a path thatsubstantially avoids perceived obstacles while generally advancing theautonomous vehicle along a roadway-based path leading to an ultimatedestination. The destination may be set according to user inputs viauser interface system 113. The navigation system may update the drivingpath dynamically while the autonomous vehicle is in operation. Thenavigation system can incorporate data from a GPS system and one or moremaps so as to determine the driving path for the autonomous vehicle.Decision module 304/planning module 305 may further include a collisionavoidance system or functionalities of a collision avoidance system toidentify, evaluate, and avoid or otherwise negotiate potential obstaclesin the environment of the autonomous vehicle. For example, the collisionavoidance system may effect changes in the navigation of the autonomousvehicle by operating one or more subsystems in control system 111 toundertake swerving maneuvers, turning maneuvers, braking maneuvers, etc.The collision avoidance system may automatically determine feasibleobstacle avoidance maneuvers on the basis of surrounding trafficpatterns, road conditions, etc. The collision avoidance system may beconfigured such that a swerving maneuver is not undertaken when othersensor systems detect vehicles, construction barriers, etc. in theregion adjacent the autonomous vehicle that would be swerved into. Thecollision avoidance system may automatically select the maneuver that isboth available and maximizes safety of occupants of the autonomousvehicle. The collision avoidance system may select an avoidance maneuverpredicted to cause the least amount of acceleration in a passenger cabinof the autonomous vehicle.

FIG. 4 is a block diagram illustrating an example of a lane boundarymodule according to one embodiment. Referring to FIG. 4, lane boundarymodule 308 can determine current lane boundaries of an ADV. Laneboundary module can include V2X communication module 401, distancedeterminer module 403, relative location determiner module 405, laneinformation retriever module 407, and trajectory generation module 409.V2X communication module 401 can transmit and/or receive a V2Xcommunication data. Distance determiner module 403 can determine adistance from the ADV to a V2X infrastructure/device based on a V2Xcommunication data and/or a ranging device. Relative location determinermodule 405 can determine a relative location of the ADV with respect tothe V2X infrastructure/device. Lane information retriever module 407 canretrieve lane configuration information. Trajectory generation module409 can generate a trajectory using the retrieved lane information tocontrol the ADV according to the trajectory.

Typically a perception module of an ADV can be used to discover roadlanes using white/yellow lane markings on road surfaces. For example,physical lane markings on a road can be extracted from camera-capturedimages using camera sensor units mounted on the ADV. However, physicallane markings can alternatively be discovered by V2X communication oncephysical lane markings and/or road lane segments are populated on adatabase. In some cases, the road lanes/lane markings populated on thedatabase can coincide with known road lanes which may have a single,both, or no physical lane lines. In one embodiment, the lane lines canbe stored on a remote database, a local database on the ADV, and/orretrieved directly from surrounding V2X enabled infrastructures.

FIG. 5 is a block diagram illustrating an example of an ADV using oneV2X module according to one embodiment. Referring to FIG. 5, in oneembodiment, example 500 includes ADV 101 along a road having lane linesL1, L2, and L3. The road may include road segment ‘A’ having lanes A1and A2. In one embodiment, ADV 101 can include V2X communication sensorS1. Once ADV 101 is within range of V2X-enabled infrastructures 501 and503, sensor S1 receives V2X communication data from infrastructures 501and 503. In one embodiment, both infrastructures 501 and 503 are locatedon a right side of ADV 101. In another embodiment, both infrastructures501 and 503 are located on a left side of ADV 101.

Note that V2X communication is a vehicular wireless communication whichmay be based on LTE and/or WLAN (e.g., WIFI) technologies, and worksdirectly between vehicles and V2X enabled devices to form an ad-hocnetwork. V2X communication can include vehicle to infrastructure (V2I),vehicle to vehicle (V2V), vehicle to pedestrian (V2P), and vehicle tonetwork (V2N). V2X communication may include common awareness messages(CAM), decentralised notification messages (DENM), or basic safetymessages (BSM). In one embodiment, a V2X communication message mayinclude a position, a velocity, or a direction of a vehicle.

In one embodiment, once ADV 101 is within range of infrastructures 501and 503, ADV 101 receives a first V2X communication data frominfrastructure 501. Based on the first V2X communication data, ADV 101obtains a first distance measurement D1, e.g., a distance between ADV101 and infrastructure 501. Distance D1 can be obtained using a rangingmethod based on a V2X, RADAR, LIDAR, and/or SONAR unit or can bedirected gathered from the V2X communication. ADV 101 receives a secondV2X communication data from infrastructure 503. Based on the second V2Xcommunication data, ADV 101 obtains a second distance measurement D2,e.g., a distance between ADV 101 and infrastructure 503. ADV 101 thenretrieves a predetermined distance D3, a distance between infrastructure501 and 503. In one embodiment, the predetermined distance D3 can beretrieved from a remote server such as server 104 of FIG. 1. In anotherembodiment, D3 can be retrieved from a local storage of ADV 101, such asV2X/lane information database 313. In another embodiment, D3 can beretrieved in the first and/or the second V2X communication data frominfrastructures 501 and/or 503.

Having knowledge of D1, D2, and D3, ADV 101 can determine a relativelocation, e.g., in x, y coordinates, of ADV 101 with respect toinfrastructures 501 and/or 503. For example, a trilateration algorithmcan be used to determine the relative location of ADV 101. In geometry,trilateration is the process of determining locations of points bymeasurement of distances of three points. In this case, assuminginfrastructure 503 is located on the (x, y)=(0, 0) coordinate, equationsto be solved for the trilateration algorithm can be written as:

x ² +y ² =D2², and x ²+(y−D3)² =D1²

where (x, y) are the coordinates of ADV 101, and D1, D2, D3 aredistances between the three points of interest (e.g., ADV 101,infrastructure 501, and infrastructure 503) and infrastructure 503 isassumed a coordinate of (x, y)=(0, 0).

In another embodiment, a triangulation algorithm can be used todetermine a relative location of ADV 101 with respect to eitherinfrastructure 501 or 503. Triangulation is the process of determiningthe location of a point by forming a triangle to it from another knownpoint. For example, relative location of ADV 101 to infrastructure 501can be determined using trigonometry based on distance D1 and angle A1.The relative coordinates of ADV 101 to infrastructure 501 can then bedetermined as (x, y)=(D1*cos(A1), D1*sin(A1)).

Once the relative location of ADV 101 is determined, ADV 101 canretrieve lane information from a remote server, a local database, ordirectly from infrastructures 501 and/or 503. The lane information caninclude information about coordinates of infrastructures 501/503, nearbylanes and their lane coordinates or relative coordinates of nearby laneswith respect to infrastructures 501/503. Based on the relative orabsolute lane coordinates, lane information, e.g., lane segments and/orlane markings, can be discovered for a current location of ADV 101. ADV101 can then generate a trajectory based on these discovered laneinformation to control the ADV autonomously according to the trajectory.Note, although lane information retrieval is illustrated usinginfrastructures 501 and 503, infrastructures 501 and 503 should not beconstrued to be limited to a physical building or a structure or afacility, but can include any V2X-enabled communication devices ormodule which may be coupled to road segments, power lines, trafficlights, etc.

FIGS. 6A-6C are examples of lane information database(s) according tosome embodiments. The lane information database may be remotedatabase(s), local database(s), or a combination thereof, which storeslane configuration information. Referring to FIGS. 6A-6C, in oneembodiment, a lane information database can include tables 600-620.Table 600 can include mapping information for infrastructures andreal-world (x, y) coordinates. Table 610 can include mapping informationfor infrastructures and nearby lane segments. Table 620 can includemapping information for lane segments and real-world (x, y) coordinates.In another embodiment, lane segments may be stored as polynomial lines.Because a relative location of an ADV to an infrastructure can bedetermined, location of lane segments (and their lane markings which maybe calculated or previously stored) can be determined relative to theADV.

FIG. 7 is a block diagram illustrating an example of an ADV using twoV2X modules according to one embodiment. Referring to FIG. 7, example700 may be similar to example 500 of FIG. 5, except in example 700, ADV101 includes V2X sensor S2. In this case, sensor S2 may be mounted neara rear portion of ADV 101 while sensor S1 may be mounted near a frontportion of ADV 101. Sensor S2 is to receive a third V2X communicationdata from infrastructure 501 and a fourth V2X communication data frominfrastructure 503. ADV 101 can determine D4 from sensor S2 toinfrastructure 501 and D5 from sensor S2 to infrastructure 503.Similarly, a trilateration algorithm can be used for the three points:sensor S2, and infrastructures 501 and 503, for corresponding distancesD3, D4, and D5, to determine a second relative location of ADV 101 toinfrastructure 501 or 503. The second relative location can be used toverify or improve an accuracy of a relative location calculated usingsensors S1, and infrastructures 501, 503. Based on the relativelocations, information (e.g., absolute and/or relative locations) oflane segments can be discovered by ADV 101. ADV 101 can then generate atrajectory based on the discovered lane segments information to controlthe ADV autonomously according to the trajectory. In one embodiment,lane segment information includes lane markings and/or lane boundariesinformation.

FIG. 8 is a block diagram illustrating an example of an ADV using twoV2X modules according to one embodiment. Referring to FIG. 8, example800 may be similar to example 500 of FIG. 5, except for example 800, ADV101 includes V2X sensor S2 and only infrastructure 501 is available forV2X communications. In this case, sensor S2 may be mounted near a rearportion of ADV 101 while sensor S1 may be mounted near a front portionof ADV 101. Sensor S1 is to receive a first V2X communication data.Sensor S2 is to receive a second V2X communication data. ADV 101determines distances D1 and D4 based on the first and the second V2Xcommunication data respectively. A trilateration algorithm can be usedbased on D1, D4, and D6, e.g., a known distance between S1 and S2, todetermine a relative location of ADV 101 with respect to infrastructures501 or 503. Based on the relative locations, information (e.g., absoluteand/or relative locations) of lane segments can be discovered by ADV101. ADV 101 can then generate a trajectory based on the discovered lanesegments information to control the ADV autonomously according to thetrajectory. In one embodiment, lane segment information includes lanemarkings and/or lane boundaries information.

FIG. 9 is a flow diagram illustrating a method according to oneembodiment. Processing 900 may be performed by processing logic whichmay include software, hardware, or a combination thereof. For example,process 900 may be performed by lane boundary module 308 of FIG. 3A.Referring to FIG. 9, at block 901, processing logic receives, at a firstsensor of the ADV, a first and a second V2X communication data from afirst infrastructure and a second infrastructure respectively. At block902, processing logic determines a first distance from the ADV to thefirst infrastructure and a second distance from the ADV to the secondinfrastructure based on the first and the second V2X communication data.At block 903, processing logic determines a relative location of the ADVto the first or the second infrastructure based on the first and thesecond distances and a predetermined distance between the firstinfrastructure and the second infrastructure. At block 904, processinglogic retrieves lane information based on the relative location of theADV to the first or the second infrastructure. At block 905, processinglogic generates a trajectory based on the lane information to controlthe ADV autonomously according to the trajectory.

In one embodiment, processing logic further receives, at a second sensorof the ADV, a third and a fourth V2X communication data from the firstinfrastructure and the second infrastructure respectively. Processinglogic determines a third distance from the ADV to the firstinfrastructure a fourth distance from the ADV to the secondinfrastructure based on the third and the fourth V2X communication data.Processing logic determines the relative location of the ADV based onthe third and fourth distances and a predetermined distance between thefirst infrastructure and the second infrastructure. Processing logicretrieves lane information based on the determined relative location ofthe ADV to the first or the second infrastructure and generates thetrajectory based on the determined lane information to control the ADVautonomously according to the trajectory.

In one embodiment, processing logic further comprising determines a laneboundary based on the lane information for the ADV and generates thetrajectory using the lane boundary to control the ADV autonomouslyaccording to the trajectory. In one embodiment, processing logic furtherreceives wirelessly, by the ADV, lane information from the firstinfrastructure. In one embodiment, processing logic further retrieveslane information from a local or a remote database according to thefirst infrastructure, where the database includes location informationof the first infrastructure.

In one embodiment, the first and the second infrastructures areinfrastructures either to a left side or to a right side of the ADV butnot both. In one embodiment, the V2X communication data include aposition, a velocity, or a direction of a vehicle or an infrastructure.

FIG. 10 is a flow diagram illustrating a method according to oneembodiment. Processing 1000 may be performed by processing logic whichmay include software, hardware, or a combination thereof. For example,process 1000 may be performed by lane boundary module 308 of FIG. 3A.Referring to FIG. 10, at block 1001, processing logic receives, at afirst sensor and a second sensor of the ADV, a first and a second V2Xcommunication data from a first infrastructure respectively. At block1002, processing logic determines a first distance from the first sensorto the first infrastructure and a second distance from the second sensorto the first infrastructure based on the first and the second V2Xcommunication data. At block 1003, processing logic determines arelative location of the ADV to the first infrastructure based on thefirst and the second distances and a predetermined distance between thefirst and the second sensors. At block 1004, processing logic retrieveslane information based on the relative location of the ADV to the firstinfrastructure. At block 1005, processing logic generates a trajectorybased on the lane information to control the ADV autonomously accordingto the trajectory.

Note that some or all of the components as shown and described above maybe implemented in software, hardware, or a combination thereof. Forexample, such components can be implemented as software installed andstored in a persistent storage device, which can be loaded and executedin a memory by a processor (not shown) to carry out the processes oroperations described throughout this application. Alternatively, suchcomponents can be implemented as executable code programmed or embeddedinto dedicated hardware such as an integrated circuit (e.g., anapplication specific IC or ASIC), a digital signal processor (DSP), or afield programmable gate array (FPGA), which can be accessed via acorresponding driver and/or operating system from an application.Furthermore, such components can be implemented as specific hardwarelogic in a processor or processor core as part of an instruction setaccessible by a software component via one or more specificinstructions.

FIG. 11 is a block diagram illustrating an example of a data processingsystem which may be used with one embodiment of the disclosure. Forexample, system 1500 may represent any of data processing systemsdescribed above performing any of the processes or methods describedabove, such as, for example, perception and planning system 110 orservers 103-104 of FIG. 1. System 1500 can include many differentcomponents. These components can be implemented as integrated circuits(ICs), portions thereof, discrete electronic devices, or other modulesadapted to a circuit board such as a motherboard or add-in card of thecomputer system, or as components otherwise incorporated within achassis of the computer system.

Note also that system 1500 is intended to show a high level view of manycomponents of the computer system. However, it is to be understood thatadditional components may be present in certain implementations andfurthermore, different arrangement of the components shown may occur inother implementations. System 1500 may represent a desktop, a laptop, atablet, a server, a mobile phone, a media player, a personal digitalassistant (PDA), a Smartwatch, a personal communicator, a gaming device,a network router or hub, a wireless access point (AP) or repeater, aset-top box, or a combination thereof. Further, while only a singlemachine or system is illustrated, the term “machine” or “system” shallalso be taken to include any collection of machines or systems thatindividually or jointly execute a set (or multiple sets) of instructionsto perform any one or more of the methodologies discussed herein.

In one embodiment, system 1500 includes processor 1501, memory 1503, anddevices 1505-1508 connected via a bus or an interconnect 1510. Processor1501 may represent a single processor or multiple processors with asingle processor core or multiple processor cores included therein.Processor 1501 may represent one or more general-purpose processors suchas a microprocessor, a central processing unit (CPU), or the like. Moreparticularly, processor 1501 may be a complex instruction set computing(CISC) microprocessor, reduced instruction set computing (RISC)microprocessor, very long instruction word (VLIW) microprocessor, orprocessor implementing other instruction sets, or processorsimplementing a combination of instruction sets. Processor 1501 may alsobe one or more special-purpose processors such as an applicationspecific integrated circuit (ASIC), a cellular or baseband processor, afield programmable gate array (FPGA), a digital signal processor (DSP),a network processor, a graphics processor, a communications processor, acryptographic processor, a co-processor, an embedded processor, or anyother type of logic capable of processing instructions.

Processor 1501, which may be a low power multi-core processor socketsuch as an ultra-low voltage processor, may act as a main processingunit and central hub for communication with the various components ofthe system. Such processor can be implemented as a system on chip (SoC).Processor 1501 is configured to execute instructions for performing theoperations and steps discussed herein. System 1500 may further include agraphics interface that communicates with optional graphics subsystem1504, which may include a display controller, a graphics processor,and/or a display device.

Processor 1501 may communicate with memory 1503, which in one embodimentcan be implemented via multiple memory devices to provide for a givenamount of system memory. Memory 1503 may include one or more volatilestorage (or memory) devices such as random access memory (RAM), dynamicRAM (DRAM), synchronous DRAM (SDRAM), static RAM (SRAM), or other typesof storage devices. Memory 1503 may store information includingsequences of instructions that are executed by processor 1501, or anyother device. For example, executable code and/or data of a variety ofoperating systems, device drivers, firmware (e.g., input output basicsystem or BIOS), and/or applications can be loaded in memory 1503 andexecuted by processor 1501. An operating system can be any kind ofoperating systems, such as, for example, Robot Operating System (ROS),Windows® operating system from Microsoft®, Mac OS®/iOS® from Apple,Android® from Google®, LINUX, UNIX, or other real-time or embeddedoperating systems.

System 1500 may further include IO devices such as devices 1505-1508,including network interface device(s) 1505, optional input device(s)1506, and other optional 10 device(s) 1507. Network interface device1505 may include a wireless transceiver and/or a network interface card(NIC). The wireless transceiver may be a WiFi transceiver, an infraredtransceiver, a Bluetooth transceiver, a WiMax transceiver, a wirelesscellular telephony transceiver, a satellite transceiver (e.g., a globalpositioning system (GPS) transceiver), or other radio frequency (RF)transceivers, or a combination thereof. The NIC may be an Ethernet card.

Input device(s) 1506 may include a mouse, a touch pad, a touch sensitivescreen (which may be integrated with display device 1504), a pointerdevice such as a stylus, and/or a keyboard (e.g., physical keyboard or avirtual keyboard displayed as part of a touch sensitive screen). Forexample, input device 1506 may include a touch screen controller coupledto a touch screen. The touch screen and touch screen controller can, forexample, detect contact and movement or break thereof using any of aplurality of touch sensitivity technologies, including but not limitedto capacitive, resistive, infrared, and surface acoustic wavetechnologies, as well as other proximity sensor arrays or other elementsfor determining one or more points of contact with the touch screen.

IO devices 1507 may include an audio device. An audio device may includea speaker and/or a microphone to facilitate voice-enabled functions,such as voice recognition, voice replication, digital recording, and/ortelephony functions. Other IO devices 1507 may further include universalserial bus (USB) port(s), parallel port(s), serial port(s), a printer, anetwork interface, a bus bridge (e.g., a PCI-PCI bridge), sensor(s)(e.g., a motion sensor such as an accelerometer, gyroscope, amagnetometer, a light sensor, compass, a proximity sensor, etc.), or acombination thereof. Devices 1507 may further include an imagingprocessing subsystem (e.g., a camera), which may include an opticalsensor, such as a charged coupled device (CCD) or a complementarymetal-oxide semiconductor (CMOS) optical sensor, utilized to facilitatecamera functions, such as recording photographs and video clips. Certainsensors may be coupled to interconnect 1510 via a sensor hub (notshown), while other devices such as a keyboard or thermal sensor may becontrolled by an embedded controller (not shown), dependent upon thespecific configuration or design of system 1500.

To provide for persistent storage of information such as data,applications, one or more operating systems and so forth, a mass storage(not shown) may also couple to processor 1501. In various embodiments,to enable a thinner and lighter system design as well as to improvesystem responsiveness, this mass storage may be implemented via a solidstate device (SSD). However in other embodiments, the mass storage mayprimarily be implemented using a hard disk drive (HDD) with a smalleramount of SSD storage to act as a SSD cache to enable non-volatilestorage of context state and other such information during power downevents so that a fast power up can occur on re-initiation of systemactivities. Also a flash device may be coupled to processor 1501, e.g.,via a serial peripheral interface (SPI). This flash device may providefor non-volatile storage of system software, including BIOS as well asother firmware of the system.

Storage device 1508 may include computer-accessible storage medium 1509(also known as a machine-readable storage medium or a computer-readablemedium) on which is stored one or more sets of instructions or software(e.g., module, unit, and/or logic 1528) embodying any one or more of themethodologies or functions described herein. Processingmodule/unit/logic 1528 may represent any of the components describedabove, such as, for example, lane boundary module 308 of FIG. 3A.Processing module/unit/logic 1528 may also reside, completely or atleast partially, within memory 1503 and/or within processor 1501 duringexecution thereof by data processing system 1500, memory 1503 andprocessor 1501 also constituting machine-accessible storage media.Processing module/unit/logic 1528 may further be transmitted or receivedover a network via network interface device 1505.

Computer-readable storage medium 1509 may also be used to store the somesoftware functionalities described above persistently. Whilecomputer-readable storage medium 1509 is shown in an exemplaryembodiment to be a single medium, the term “computer-readable storagemedium” should be taken to include a single medium or multiple media(e.g., a centralized or distributed database, and/or associated cachesand servers) that store the one or more sets of instructions. The terms“computer-readable storage medium” shall also be taken to include anymedium that is capable of storing or encoding a set of instructions forexecution by the machine and that cause the machine to perform any oneor more of the methodologies of the present disclosure. The term“computer-readable storage medium” shall accordingly be taken toinclude, but not be limited to, solid-state memories, and optical andmagnetic media, or any other non-transitory machine-readable medium.

Processing module/unit/logic 1528, components and other featuresdescribed herein can be implemented as discrete hardware components orintegrated in the functionality of hardware components such as ASICS,FPGAs, DSPs or similar devices. In addition, processingmodule/unit/logic 1528 can be implemented as firmware or functionalcircuitry within hardware devices. Further, processing module/unit/logic1528 can be implemented in any combination hardware devices and softwarecomponents.

Note that while system 1500 is illustrated with various components of adata processing system, it is not intended to represent any particulararchitecture or manner of interconnecting the components; as suchdetails are not germane to embodiments of the present disclosure. Itwill also be appreciated that network computers, handheld computers,mobile phones, servers, and/or other data processing systems which havefewer components or perhaps more components may also be used withembodiments of the disclosure.

Some portions of the preceding detailed descriptions have been presentedin terms of algorithms and symbolic representations of operations ondata bits within a computer memory. These algorithmic descriptions andrepresentations are the ways used by those skilled in the dataprocessing arts to most effectively convey the substance of their workto others skilled in the art. An algorithm is here, and generally,conceived to be a self-consistent sequence of operations leading to adesired result. The operations are those requiring physicalmanipulations of physical quantities.

It should be borne in mind, however, that all of these and similar termsare to be associated with the appropriate physical quantities and aremerely convenient labels applied to these quantities. Unlessspecifically stated otherwise as apparent from the above discussion, itis appreciated that throughout the description, discussions utilizingterms such as those set forth in the claims below, refer to the actionand processes of a computer system, or similar electronic computingdevice, that manipulates and transforms data represented as physical(electronic) quantities within the computer system's registers andmemories into other data similarly represented as physical quantitieswithin the computer system memories or registers or other suchinformation storage, transmission or display devices.

Embodiments of the disclosure also relate to an apparatus for performingthe operations herein. Such a computer program is stored in anon-transitory computer readable medium. A machine-readable mediumincludes any mechanism for storing information in a form readable by amachine (e.g., a computer). For example, a machine-readable (e.g.,computer-readable) medium includes a machine (e.g., a computer) readablestorage medium (e.g., read only memory (“ROM”), random access memory(“RAM”), magnetic disk storage media, optical storage media, flashmemory devices).

The processes or methods depicted in the preceding figures may beperformed by processing logic that comprises hardware (e.g. circuitry,dedicated logic, etc.), software (e.g., embodied on a non-transitorycomputer readable medium), or a combination of both. Although theprocesses or methods are described above in terms of some sequentialoperations, it should be appreciated that some of the operationsdescribed may be performed in a different order. Moreover, someoperations may be performed in parallel rather than sequentially.

Embodiments of the present disclosure are not described with referenceto any particular programming language. It will be appreciated that avariety of programming languages may be used to implement the teachingsof embodiments of the disclosure as described herein.

In the foregoing specification, embodiments of the disclosure have beendescribed with reference to specific exemplary embodiments thereof. Itwill be evident that various modifications may be made thereto withoutdeparting from the broader spirit and scope of the disclosure as setforth in the following claims. The specification and drawings are,accordingly, to be regarded in an illustrative sense rather than arestrictive sense.

What is claimed is:
 1. A computer-implemented method to generate adriving trajectory for an autonomous driving vehicle (ADV), the methodcomprising: receiving, at a first sensor of the ADV, a first and asecond V2X communication data from a first infrastructure and a secondinfrastructure respectively; determining a first distance from the ADVto the first infrastructure and a second distance from the ADV to thesecond infrastructure based on the first and the second V2Xcommunication data; determining a relative location of the ADV to thefirst or the second infrastructure based on the first and the seconddistances and a predetermined distance between the first infrastructureand the second infrastructure; retrieving lane information based on therelative location of the ADV to the first or the second infrastructure;and generating a trajectory based on the lane information to control theADV autonomously according to the trajectory.
 2. The method of claim 1,further comprising: receiving, at a second sensor of the ADV, a thirdand a fourth V2X communication data from the first infrastructure andthe second infrastructure respectively; determining a third distancefrom the ADV to the first infrastructure and a fourth distance from theADV to the second infrastructure based on the third and the fourth V2Xcommunication data; determining the relative location of the ADV basedon the third and fourth distances and a predetermined distance betweenthe first infrastructure and the second infrastructure; retrieving laneinformation based on the determined relative location of the ADV to thefirst or the second infrastructure; and generating the trajectory basedon the determined lane information to control the ADV autonomouslyaccording to the trajectory.
 3. The method of claim 1, furthercomprising: determining a lane boundary based on the lane informationfor the ADV; and generating the trajectory using the lane boundary tocontrol the ADV autonomously according to the trajectory.
 4. The methodof claim 1, further comprising receiving wirelessly, by the ADV, laneinformation from the first infrastructure.
 5. The method of claim 1,further comprises retrieving lane information from a local or a remotedatabase according to the first infrastructure, wherein the databaseincludes a location information of the first infrastructure.
 6. Themethod of claim 1, wherein the first and the second infrastructures areinfrastructures either to a left side or to a right side of the ADV butnot both.
 7. The method of claim 1, wherein the V2X communication datainclude a position, a velocity, or a direction of a vehicle or aninfrastructure.
 8. A non-transitory machine-readable medium havinginstructions stored therein, which when executed by a processor, causethe processor to perform operations, the operations comprising:receiving, at a first sensor of the ADV, a first and a second V2Xcommunication data from a first infrastructure and a secondinfrastructure respectively; determining a first distance from the ADVto the first infrastructure and a second distance from the ADV to thesecond infrastructure based on the first and the second V2Xcommunication data; determining a relative location of the ADV to thefirst or the second infrastructure based on the first and the seconddistances and a predetermined distance between the first infrastructureand the second infrastructure; retrieving lane information based on therelative location of the ADV to the first or the second infrastructure;and generating a trajectory based on the lane information to control theADV autonomously according to the trajectory.
 9. The non-transitorymachine-readable medium of claim 8, further comprising: receiving, at asecond sensor of the ADV, a third and a fourth V2X communication datafrom the first infrastructure and the second infrastructurerespectively; determining a third distance from the ADV to the firstinfrastructure and a fourth distance from the ADV to the secondinfrastructure based on the third and the fourth V2X communication data;determining the relative location of the ADV based on the third andfourth distances and a predetermined distance between the firstinfrastructure and the second infrastructure; retrieving laneinformation based on the determined relative location of the ADV to thefirst or the second infrastructure; and generating the trajectory basedon the determined lane information to control the ADV autonomouslyaccording to the trajectory.
 10. The non-transitory machine-readablemedium of claim 8, further comprising: determining a lane boundary basedon the lane information for the ADV; and generating the trajectory usingthe lane boundary to control the ADV autonomously according to thetrajectory.
 11. The non-transitory machine-readable medium of claim 8,further comprising receiving wirelessly, by the ADV, lane informationfrom the first infrastructure.
 12. The non-transitory machine-readablemedium of claim 8, further comprises retrieving lane information from alocal or a remote database according to the first infrastructure,wherein the database includes a location information of the firstinfrastructure.
 13. The non-transitory machine-readable medium of claim8, wherein the first and the second infrastructures are infrastructureseither to a left side or to a right side of the ADV but not both. 14.The non-transitory machine-readable medium of claim 8, wherein the V2Xcommunication data include a position, a velocity, or a direction of avehicle or an infrastructure.
 15. A data processing system, comprising:a processor; and a memory coupled to the processor to storeinstructions, which when executed by the processor, cause the processorto perform operations, the operations including: receiving, at a firstsensor of the ADV, a first and a second V2X communication data from afirst infrastructure and a second infrastructure respectively;determining a first distance from the ADV to the first infrastructureand a second distance from the ADV to the second infrastructure based onthe first and the second V2X communication data; determining a relativelocation of the ADV to the first or the second infrastructure based onthe first and the second distances and a predetermined distance betweenthe first infrastructure and the second infrastructure; retrieving laneinformation based on the relative location of the ADV to the first orthe second infrastructure; and generating a trajectory based on the laneinformation to control the ADV autonomously according to the trajectory.16. The system of claim 15, further comprising: receiving, at a secondsensor of the ADV, a third and a fourth V2X communication data from thefirst infrastructure and the second infrastructure respectively;determining a third distance from the ADV to the first infrastructureand a fourth distance from the ADV to the second infrastructure based onthe third and the fourth V2X communication data; determining therelative location of the ADV based on the third and fourth distances anda predetermined distance between the first infrastructure and the secondinfrastructure; retrieving lane information based on the determinedrelative location of the ADV to the first or the second infrastructure;and generating the trajectory based on the determined lane informationto control the ADV autonomously according to the trajectory.
 17. Thesystem of claim 15, further comprising: determining a lane boundarybased on the lane information for the ADV; and generating the trajectoryusing the lane boundary to control the ADV autonomously according to thetrajectory.
 18. The system of claim 15, further comprising receivingwirelessly, by the ADV, lane information from the first infrastructure.19. The system of claim 15, further comprises retrieving laneinformation from a local or a remote database according to the firstinfrastructure, wherein the database includes a location information ofthe first infrastructure.
 20. The system of claim 15, wherein the firstand the second infrastructures are infrastructures either to a left sideor to a right side of the ADV but not both.
 21. The system of claim 15,wherein the V2X communication data include a position, a velocity, or adirection of a vehicle or an infrastructure.